A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering and in-cloud distribution to specialized medical personnel. As such it presents trade-offs typical of other Cyber Physical System, where hardware, algorithms and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential, called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects (n=12 healthy, n=3 Mildly Cognitive Impaired (MCI) and n=2 with Alzheimer Disease (AD) involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s) yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.

A Mobile Health System for Neurocognitive Impairment Evaluation based on P300 Detection

De Venuto, D.;Mezzina, G.;Scioscia, F.;Ruta, M.;Di Sciascio, E.;
2018

Abstract

A new mobile healthcare system for neuro-cognitive function monitoring and treatment is presented. The architecture of the system features sensors to measure the brain potential, localized data analysis and filtering and in-cloud distribution to specialized medical personnel. As such it presents trade-offs typical of other Cyber Physical System, where hardware, algorithms and software implementations have to come together in a coherent fashion. The system is based on spatio-temporal detection and characterization of a specific brain potential, called P300. The diagnosis of cognitive deficit is achieved by analyzing the data collected by the system with a new algorithm called tuned-Residue Iteration Decomposition (t-RIDE). The system has been tested on 17 subjects (n=12 healthy, n=3 Mildly Cognitive Impaired (MCI) and n=2 with Alzheimer Disease (AD) involved in three different cognitive tasks with increasing difficulty. The system allows fast diagnosis of cognitive deficit, including mild and heavy cognitive impairment: t-RIDE convergence is achieved in 79 iterations (i.e., 1.95s) yielding an 80% accuracy in P300 amplitude evaluation with only 13 trials on a single EEG channel.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/115750
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